import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def 				lowerCAmelCase__       (			)      ->List[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser()
  parser.add_argument(
      "-m"    ,     "--pretrained_model_name_or_path"    ,     type=a__    ,     default=a__    ,     required=a__    ,     help="Path to pretrained model or model identifier from huggingface.co/models."    ,     )
  parser.add_argument(
      "-c"    ,     "--caption"    ,     type=a__    ,     default="robotic cat with wings"    ,     help="Text used to generate images."    ,     )
  parser.add_argument(
      "-n"    ,     "--images_num"    ,     type=a__    ,     default=4    ,     help="How much images to generate."    ,     )
  parser.add_argument(
      "-s"    ,     "--seed"    ,     type=a__    ,     default=42    ,     help="Seed for random process."    ,     )
  parser.add_argument(
      "-ci"    ,     "--cuda_id"    ,     type=a__    ,     default=0    ,     help="cuda_id."    ,     )
  _UpperCamelCase           =    parser.parse_args()
  return args


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if not len(a__ ) == rows * cols:
    raise ValueError("The specified number of rows and columns are not correct." )

  _UpperCamelCase      ,						_UpperCamelCase           =    imgs[0].size
  _UpperCamelCase           =    Image.new("RGB"    ,     size=(cols * w, rows * h) )
  _UpperCamelCase      ,						_UpperCamelCase           =    grid.size

  for i, img in enumerate(a__ ):
    grid.paste(a__    ,     box=(i % cols * w, i // cols * h) )
  return grid


def 				lowerCAmelCase__       (			a__    ,     a__="robotic cat with wings"    ,     a__=7.5    ,     a__=50    ,     a__=1    ,     a__=42    ,     )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    torch.Generator(pipeline.device ).manual_seed(a__ )
  _UpperCamelCase           =    pipeline(
      a__    ,     guidance_scale=a__    ,     num_inference_steps=a__    ,     generator=a__    ,     num_images_per_prompt=a__    ,     ).images
  _UpperCamelCase           =    int(math.sqrt(a__ ) )
  _UpperCamelCase           =    image_grid(a__    ,     rows=_rows    ,     cols=num_images_per_prompt // _rows )
  return grid, images


lowerCamelCase__  =					parse_args()
# Load models and create wrapper for stable diffusion
lowerCamelCase__  =					CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
lowerCamelCase__  =					CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
lowerCamelCase__  =					AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
lowerCamelCase__  =					UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

lowerCamelCase__  =					StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCamelCase__  =					lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
   lowerCamelCase__  =					load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, '''unet''', unet)
else:
   lowerCamelCase__  =					unet.to(torch.device('''cuda''', args.cuda_id))
lowerCamelCase__  =					pipeline.to(unet.device)
lowerCamelCase__,lowerCamelCase__  =					generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
lowerCamelCase__  =					os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
   image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




